Unraveling primary production dynamics: A study of a semi-arid ecosystem in the slope and watershed scales.
Svoray, Tal*,1, Shafran-Natan, Rakefet1, Perevolotsky, Avi2, 1 Ben-Gurion University of the Negev, Beer-Sheva, Israel2 Agricultural Research Organization - The Volcani Centre, Bet-Dagan, Israel
ABSTRACT- Herbaceous vegetation production (HVP) plays a key role in understanding ecosystem functioning. In semi-arid environments HVP responds significantly to spatial and temporal changes in rainfall amounts and soil moisture distribution. Therefore, semi-arid regions are characterized by patchy pattern that was not yet fully explained in the detailed scale. The main reason for our limited understanding is the focus of scientific effort in two polarized scales - small plot and large regions - rather then in a detailed study of the intermediate scale (slope and watershed). We present spatially and temporally explicit model to predict potential HVP in both ends of a semi-arid region. The model is GIS-based and the hypotheses of soil water requirements for potential HVP are formulated using fuzzy logic. The model simulates spatial and temporal variation in four indirect variables: rock cover, radiation, runoff and soil sub-slope units; and in two climatic variables: temperature and rainfall amount. The radiation, sub-slope units and runoff were predicted using digital elevation data, while soil characteristics, extracted from field survey, were combined in the runoff model. Rock coverage was classified from air photos and the climate data is from standard meteorological stations. The model allows prediction of two processes in the life span of the herbaceous plant: 1) germination - modeled in daily iterations until the conditions are satisfied; 2) primary production - modeled on a weekly basis. Finally, seasonal HVP conditions in every cell were predicted. IKONOS data and field data, including herbaceous biomass and soil moisture measurements in three seasons were used to validate the model in two sites (300 and 550 mm). Preliminary results show that the model was advantageous in habitat prediction comparing to previous field surveys. The indirect variables were not intercorrelated and therefore all should participate in the model. Non of the variables could explain solely the variation in HVP nor a simple multiple regression of all variables. Our mechanistic model was more succesful in predicting HVP and it revealed that the effect of rock cover is more important on south-facing slopes while redistribution of water due to overland flow is more dominant in north-facing slopes.
Key words: Habitat prediction, Geographic information system, Fuzzy logic, Remote sensing
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